Prof. Guibas pointed out
that, across science, engineering, medicine and business we faced a deluge of
data coming from sensors, simulations or activities of individuals on the internet,
and the data often contain geometric and/or visual characters. Furthermore, the
geometric data sets we collected were frequently highly correlated, reflecting
information about the same or similar entities, or echoing semantically
important repetitions/symmetries or hierarchical structures common to both
man-made and natural objects. He described general mathematical and
computational tools for the construction, analysis and exploitation of such
relational networks. By creating societies of data sets and their associations
in a globally consistent way, we enabled a certain joint understanding of the
data that provided the powers of abstraction, analogy, compression, error
correction and summarization. Faculty and students were greatly attracted to
the intriguing images Prof. Guibas showed during the talk, and had extensive
discussion with him afterwards.

Bio:

Leonidas Guibas obtained his Ph.D. from Stanford in 1976,
under the supervision of Donald Knuth. His subsequent employers include Xerox
PARC, MIT, and DEC/SRC. He has been at Stanford since 1984 as Professor of
Computer Science. He has produced several Ph.D. students who are well-known in
computational geometry, such as John Hershberger, Jack Snoeyink and Jorge
Stolfi, or in computer graphics, such as David Salesin, Eric Veach and Niloy
Mitra.